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Isran K. Hasan
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Department of Statistics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo Jl. Prof. Dr. Ing. B.J Habibie, Tilongkabila Kabupaten Bone Bolango, 96119
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INDONESIA
JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Published by Universitas Negeri Gorontalo
ISSN : -     EISSN : 27227189     DOI : https://doi.org/10.37905/jjps
Core Subject : Science, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Social Sciences
Probability Theory Mathematical Statistics Computational Statistics Stochastic Processes Financial Statistics Bayesian Analysis Survival Analysis Time Series Analysis Neural Network Another field which is related to statistics and the applications Another field which is related to Probability and the application
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 67 Documents
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Model Markov Switching Autoregressive pada Data Covid-19 di Indonesia Rizki, Setyo Wira; Martha, Shantika; Bartolomius, Bartolomius; Apriliyanti, Rita
Jambura Journal of Probability and Statistics Vol 5, No 1 (2024): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i1.19429

Abstract

The Covid-19 pandemic has had a very influential impact on socio-economic conditions in Indonesia. Forecasting the number of Covid-19 cases is needed to support taking preventive action. The method that can be used to determine the number of Covid-19 cases is a forecasting method using the Markov Switching Autoregressive (MSAR) time series data model as an alternative for analyzing structural change data. This research uses Covid-19 confirmation data in Indonesia for the period March 2020-June 2021, with the aim of designing an MSAR model and calculating the magnitude of the transition opportunity in each state in the Covid-19 confirmation data in Indonesia. The MSAR model begins by describing the data and checking the stationarity of the data. After that, Box-Jenkins modeling was carried out to test heteroskedasticity and structural changes. Next, the MSAR model parameters were estimated and the transition matrix was formed. This research shows that the best MSAR model formed is the MS (2)-AR (5) model, with a static transition probability value in state 1 of 0.981330. However, it appears that there is a chance of 0.018670 for the Covid-19 confirmation condition to move to state 2. Testing in the case of state 2 produces a transition chance of 0.980991 in state 2, with a transition chance of Covid-19 confirmation changing to state 1 of 0.019009.
Pemodelan Kadar Hemoglobin pada Pasien Demam Berdarah di Kota Samarinda Menggunakan Regresi Semiparametrik Spline Truncated Dani, Andrea Tri Rian; Putra, Fachrian Bimantoro; Zen, Muhammad Aldani; Sifriyani, Sifriyani; Fauziyah, Meirinda; Ratnasari, Vita; Adrianingsih, Narita Yuri
Jambura Journal of Probability and Statistics Vol 4, No 2 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v4i2.18923

Abstract

This article discusses the innovation of statistical modeling in regression analysis with a semiparametric approach applied to health data. Regression analysis is a method in statistics that takes a lot of roles in statistical modeling. Regression analysis is used to model the relationship between the independent variable (x) and the dependent variable (y). There are three approaches to regression analysis, namely parametric, nonparametric, and a combination of the two, namely semiparametric. Semiparametric regression is used when the dependent variable has a known relationship with some of the independent variables and has an unknown pattern of a relationship with some of the other independent variables. The purpose of this study was to model hemoglobin levels in dengue fever patients, with the independent variables used being the number of hematocrits (x1) and the number of leukocytes (x2). The method used is spline truncated semiparametric regression. The truncated spline estimator was chosen for the nonparametric component because it has many advantages in modeling, one of which is being able to model patterns where the form of the relationship is unknown. The parameter estimation used is the maximum estimation. Selection of the optimal knot point using Generalized Cross-Validation (GCV). Based on the results of the analysis, the truncated spline semiparametric regression model was obtained which was applied to the hemoglobin level data in a model with three knots which have a coefficient of determination of 89.074%. Based on the results of testing the hypothesis simultaneously, it can be concluded that simultaneously the independent variable has a significant effect on the dependent variable. In the partial test, it is concluded that the variables x1 and x2 have a significant influence on the dependent variable y .
Penerapan Principal Component Analysis untuk Reduksi Variabel pada Algoritma K-Means Clustering Rosyada, Istina Alya; Utari, Dina Tri
Jambura Journal of Probability and Statistics Vol 5, No 1 (2024): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i1.18733

Abstract

K-Means clustering is a widely used clustering algorithm. However, it has the disadvantage that the performance of clustering data decreases if the variables of the processed data are immense. The complex variables problem in K-Means can be overcome by combining the Principal Component Analysis (PCA) variable reduction method. This study uses seven indicator variables for the welfare of the people of West Java Province in 2021 to measure the welfare level of districts/cities. The results of the analysis obtained two principal components based on eigenvalues. Clustering from cluster analysis with the K-Means with variable reduction using PCA formed the three best clusters where the number of members of each cluster consisted of 12, 8, and 7 districts/cities.
Bayes Estimator of Exponential Distribution Parameters of Type II Censored Data with Linear Exponential Loss Function Method Based on Jeffrey Priors Previan, Anggara Teguh; Kurniawan, Ardi; Mardianto, M. Fariz Fadillah; Sediono, Sediono
Jambura Journal of Probability and Statistics Vol 4, No 2 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v4i2.22549

Abstract

Survival analysis is often used in the application of analyzing the survival of an object such as living things or objects. This analysis is identical to data censoring which is divided into three, namely: type I, II, and III censored data. Type II censored data is data censoring done by determining the number of objects to be analyzed  from the total number of observation objects . Type II censored data is used when the analysis is intended to maximize the results of the analysis. Bayesian Linear Exponential (LINEX) loss function is one method that can be used to estimate parameters in survival analysis by minimizing the expected value of LINEX. The purpose of this study is to determine the Bayesian LINEX loss function parameter estimation on type II censored data using exponential distribution. This method uses the concept of posterior distribution and prior distribution. The prior distribution used is the Jeffrey prior distribution which has objective properties and is based on Fisher information theory The application of the parameter estimation results is carried out on the survival data of lung cancer patients obtained from the North Central Cancer Treatment Group. Based on the results of parameter estimation, it is concluded that the greater the value of the controller  (a) can produce a smaller value of parameter estimation results (θ^) . The results of this study can be used as a reference in conducting survival tests using type II censored exponential distribution data using the LINEX loss function method based on Jeffrey priors.
Estimasi Risiko Pada Saham PT. Gojek Tokopedia Tbk dan Expected Shortfall Menggunakan ARIMA-GARCH Model Amri, Ihsan Fathoni; Puspitasari, Linda; Priambodo, Danu; Azzahrani, Rahma Dewi; Haris, M. Al
Jambura Journal of Probability and Statistics Vol 5, No 2 (2024): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i2.22552

Abstract

Evaluation of losses is very important when investing in stocks where an approach is needed to take into account risk, the approaches that can be used are Value-at-Risk and Expected Shortfall. The purpose of this research is to estimate the Value-at-Risk and Expected Shortfall of PT. Gojek Tokopedia Tbk uses the time series model methodology. One year daily closing price of PT. Gojek Tokopedia Tbk will be used as a source of research data. During the time series modeling process, the ARIMA model is intended as an average model and the GARCH model for model volatility, both of which are used to predict stock movements. The average value and variance models are then intended to calculate the Value-at-Risk and Expected Shortfall of the stocks used, respectively. The results obtained for the VaR value were 0.088911 and the ES value was 0.122084. This shows that the ES method is superior in considering the risk of stock investment that has been analyzed. 
Analisis Komparasi Performa Metode Double Exponential Smoothing Tipe Holt Dan Double Moving Average Untuk Peramalan Jumlah Penduduk Miskin Di Provinsi Maluku Kondo Lembang, Ferry; Makatita, Romy; Haumahu, Gabriella; Lewaherilla, Norisca
Jambura Journal of Probability and Statistics Vol 5, No 2 (2024): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i2.23157

Abstract

The aim of this research is to compare the performance of the Holt type double exponential smoothing method and the double moving average method to predict the number of poor people in Maluku Province. The performance of these two forecasting methods was implemented on data on the number of poor people in Maluku Province from 2010 to 2021. The Holt type double exponential smoothing method and the double moving average method are often used as forecasting tools for non-stationary, non-seasonal and trend data types because they have The best level of accuracy is for time series data such as data on the number of poor people in Maluku Province. The results of a comparative analysis of the performance of the two methods based on the criteria for the smallest MAPE value, it was found that the Holt type double exponential smoothing method had better performance than the double moving average method for predicting the number of poor people in Maluku Province, producing the smallest MAPE value of 4.096. The forecast results for the number of poor people in Maluku Province for 2022 is 283.66 thousand people and for 2023 it is 276.78 thousand people. 
Proyeksi Indeks Pembangunan Berwawasan Kependudukan (IPBK) Menggunakan Single Dan Double Exponential Smoothing (Studi Di Kabupaten Gorontalo 2024 – 2026) Akolo, Ingka Rizkyani; Umar, Razak; Yasin, Indri Afriani
Jambura Journal of Probability and Statistics Vol 5, No 2 (2024): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i2.24854

Abstract

The Population-Oriented Development Index (IPBK) is an important indicator for measuring regional development success, focusing on human resource quality. This study aims to project the IPBK for 2024–2026 and identify the best method for projecting the IPBK in Gorontalo Regency. The projection methods used are single and double exponential smoothing. The IPBK data for Gorontalo Regency was obtained from the 2024 BKKBN Central Publication. The variables include participation (X1), sustainability (X2), inclusiveness (X3), holistic integration (X4), equality (X5), and IPBK (X6). The data was analyzed using Minitab 16 software. The results indicate that the double exponential smoothing method outperforms the single exponential smoothing method for projecting the IPBK and its dimensions in Gorontalo Regency, achieving a MAPE value of less than 10\%. The IPBK projections and its dimensions for 2024–2026 using the double exponential smoothing method show an annual upward trend, predicting that by 2025–2026, Gorontalo Regency's IPBK status will rise from category 3 (upper-middle status) to category 4 (high status). This high status indicates that Gorontalo Regency has achieved significant progress in key indicators reflecting the welfare and quality of life of its residents. 
Pemodelan Persamaan Struktural Kemampuan Akademik dan Karakteristik Individu dalam Mengidentifikasi Ketertarikan Siswa Kelas XII dalam Memilih Perguruan Tinggi Negeri Sopbaba, Merlina; Simarmata, Justin Eduardo; Klau, Kondradus Yohanes
Jambura Journal of Probability and Statistics Vol 5, No 2 (2024): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i2.24914

Abstract

State universities represent a primary option for prospective students aspiring to pursue higher education. During the selection process of a state university, prospective students are influenced by various factors, including academic prowess, individual traits, and personal considerations pertinent to their chosen state university. This study was conducted among 59 twelfth-grade students at SMA Kristen Petra Kefamenanu during the academic year 2023-2024. The methodological framework of structural equation modeling (SEM) was utilized to investigate the interrelationships among these variables. The analysis revealed that students' academic aptitude, comprising elements such as self-assurance, scholastic achievements, active participation, and innovative thinking capabilities, exerts a significant impact on their inclination towards selecting state universities, with a path coefficient of 0.40. Moreover, individual attributes such as autonomy, creativity, integrity, courage, and perseverance also demonstrate a substantial influence, yielding a path coefficient of 0.84 on the inclination towards selecting state universities. These findings underscore the pivotal roles played by both academic capabilities and individual attributes in the decision-making process surrounding state university selection. These findings indicate that both academic ability and individual characteristics play important roles in the decision to choose state universities. These results can serve as a guide for educational institutions and parents to better understand the factors influencing students' decisions in choosing higher education institutions and can help in developing more effective new student admission strategies. 
Faktor Yang Berpengaruh Terhadap Kematian Bayi Baru Lahir Di Daerah Kepulauan Alor Adrianingsih, Narita Yuri; Hinadang, Elen A.; Dani, Andrea Tri Rian; Novitasari, Nilam
Jambura Journal of Probability and Statistics Vol 5, No 2 (2024): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i2.19432

Abstract

Binary logistic regression is an analysis that aims to determine the relationship between one or more predictor variables that are quantitative, qualitative, or a combination of both to a dichotomous response variable with two categories. Binary logistic regression analysis can also be applied in the health sector, especially in newborns' dead or alive status. Infant deaths in Indonesia, especially in the Alor Islands, are still widespread, which is due to several factors. In this study, several variables are thought to influence the status of the newborn, namely the newborn's weight, the baby's body length, the baby's gender, asphyxia, the mother's systolic blood pressure, and the mother's age at birth. The results of the analysis from this research showed that the factor that influences the death of newborn babies in the Alor Islands area is asphyxia. Newborn babies who experience asphyxia are 109,947 times more likely to die compared to babies who do not experience asphyxia.  
Analisis Regresi Probit dalam Kasus Usia Datangnya Menarche Studi Kasus Remaja di Kabupaten Jember Habibi, Azwar
Jambura Journal of Probability and Statistics Vol 6, No 1 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i1.18418

Abstract

Probit regression is an alternative log-linear approach to handle the dependent variable category analysis. Probit regression that can be used in experimental data, so you want to be applied to the survey data on the age of the first menstrual coming in teenage women. Each woman has a different age of menarche (first menstruation) and cannot be ascertained. The goal to be achieved to answer the problem is to find out the probit regression equation obtained from the age data of menarche in adolescent women so that the value of changes in variables is not free to changes in each unit of independent variables such as linear models. To find out the probability of menarche at a certain age. To find out the age of the coming menarche the most experienced by teenage women. Starting from the problems we have explained before, several conclusions can be drawn including the probit regression equation obtained for the age of menarche in teenage women, namely: $y = -11,8189 + 0,907823 X$. The model is good enough because the parameter value Significant/ meaningful regression is not equal to 0 (zero). The probability of menarche at the age of 9.2 years has the greatest value among all the ages of teenage women, equal to 0,999728. The age of menarche comes at most at the age of 17,5 years. 

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